1887
Volume 67, Issue 3
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Integration of all available data in reservoir characterization is critically important. 2D mapping is a reliable and robust technique that allows integration of multiple secondary data, including geological and geophysical surfaces and maps, to generate realistic summaries of reservoir quality at each location in an area of interest with an associated measure of uncertainty. This is achieved in 2D mapping with a more straightforward implementation, requiring significantly less time and fewer resources than three‐dimensional modelling. In this paper, we propose an approach for the empirical calculation and optimization of differential compaction maps by leveraging existing well control for the use in 2D mapping. Success of the proposal is demonstrated through tests of accuracy, precision and fairness of the local uncertainty distributions for 100 new stratigraphical wells drilled in the Christina Lake and Kirby East area.

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2019-02-22
2024-04-19
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References

  1. AthyL.F.1930. Density, porosity and compaction of sedimentary rocks. American Association of Petroleum Geologists Bulletin31, 241–287.
    [Google Scholar]
  2. BabakO. and DeutschC.V.2009a. An intrinsic model of coregionalization that solves variance inflation in collocated cokriging. Computers and Geosciences35, 603–614.
    [Google Scholar]
  3. BabakO. and DeutschC.V.2009b. Improved spatial modeling by merging multiple secondary data for intrinsic collocated cokriging. Journal of Petroleum Science and Engineering69, 93–99.
    [Google Scholar]
  4. BarnettR.M. and DeutschC.V.2013. Alternative to Bayesian updating/P‐field mapping. Centre for Computational Geostatistics, Report 15, 203, 1–12.
  5. ButlerR.M.1991. Thermal Recovery of Oil and Bitumen. Prentice Hall.
    [Google Scholar]
  6. CarrigyM.A.1959. Geology of the McMurray formation: General geology of the McMurray area, Part 3. Volume 1 of Alberta Research Council. Memoir.
  7. DeutschC.V.1996. Direct assessment of local accuracy and precision. In: Geostatistics, Vol. 1 (eds BaafiE.Y. and SchofieldN.A. ), pp. 115–125. Kluwer Academic Publishers.
    [Google Scholar]
  8. Deutsch C.V. and Journel
    Deutsch C.V. and Journel A.G. 1998. GSLIB: Geostatistical Software Library and User's Guide. Oxford University Press.
    [Google Scholar]
  9. DeutschC.V., RenW.S. and LeuangthongO.2005. Joint uncertainty assessment with a combined Bayesian Updating/LU/P‐field approach. GIS and Spatial Analysis‐–2005 Annual Conference of the International Association for Mathematical Geology, pp. 639–644.
  10. DoyenP.M., den BoerL.D. and PilleyW.R.1996. S et al. 1996. Seismic porosity mapping in the Ekofisk Field using a new form of collocated cokriging. SPE 36498, SPE Annual Technical Conference and Exhibition.
  11. Gallop
    Gallop , 2007. Inferring reservoir lithology from sediment compaction. Presented at the SEG Development and Production Forum, Edmonton, Canada.
  12. GoovaertsP.1997. Geostatistics for Natural Resources Evaluation. Oxford University Press.
    [Google Scholar]
  13. HeinF.J. and CotterillD.K.2006. The Athabasca oil sands—a regional geological perspective, Fort McMurray area, Alberta, Canada. Natural Resources Research15, 85–102.
    [Google Scholar]
  14. HolbrookP.2002. The primary controls over sediment compaction. In: Pressure Regimes in Sedimentary Basins and Their Predictions (eds A.R.Huffman and G.LBowers ), pp. 21–32. AAPG Memior 76.
    [Google Scholar]
  15. JoH.R. and HaC.G.2013. Stratigraphic architecture of fluvial deposits of the Cretaceous McMurray Formation, Athabasca oil sands, Alberta, Canada. Geosciences Journal17, 417–427.
    [Google Scholar]
  16. McKinleyJ.M., DeutschC.V., NeufeldC., PattonM., CooperM. and YoungM.E.2014. Use of geostatistical Bayesian updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration. The Journal of the South African Institute of Mining and Metallurgy144, 575–586.
    [Google Scholar]
  17. PyrczM.J. and DeutschC.V.2014. Geostatistical Reservoir Modeling. Oxford University Press.
    [Google Scholar]
  18. RangerM.J. and PembertonS.G.1997. Elements of a stratigraphic framework for the McMurray Formation in south Athabasca area, Alberta. In: Petroleum Geology of the Cretaceous Mannville Group, Western Canada (eds S.G.Pemberton and D.P.James ), pp. 263–291. Canadian Society of Petroleum Geologists, Memoir 18.
    [Google Scholar]
  19. RenW., DeutschC.V., GarnerD., WheelerT.J., RichyJ.‐F. and MusE.2008. Quantifying resources for the Surmont Lease with 2D mapping and multivariate statistics. SPE Reservoir Evaluation & Engineering, 11, 341–351.
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): Numerical modelling; Optimization

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